DroneSilient (drone + resilient): an anti-drone system
Abstract It is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. T...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-10-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-024-01004-6 |
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| author | Meghna Manoj Nair Harini Sriraman Gadiparthy Harika Sai V. Pattabiraman |
| author_facet | Meghna Manoj Nair Harini Sriraman Gadiparthy Harika Sai V. Pattabiraman |
| author_sort | Meghna Manoj Nair |
| collection | DOAJ |
| description | Abstract It is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality. |
| format | Article |
| id | doaj-art-984802f4a610469687ae9b98f9d2cec8 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-984802f4a610469687ae9b98f9d2cec82025-08-20T02:11:26ZengSpringerOpenJournal of Big Data2196-11152024-10-0111111510.1186/s40537-024-01004-6DroneSilient (drone + resilient): an anti-drone systemMeghna Manoj Nair0Harini Sriraman1Gadiparthy Harika Sai2V. Pattabiraman3School of Computer Science and Engineering, Vellore Institute of Technology (VIT), ChennaiSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), ChennaiSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), ChennaiSchool of Computer Science and Engineering, Vellore Institute of Technology (VIT), ChennaiAbstract It is imperative to take a holistic strategy to thwarting drone threats, including the identification of drones and drone-like gadgets like ornithopters that visually imitate birds. In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. A modified bloom filter method is used to further identify the recognized object after a drone-like object has been found, allowing for the differentiation between regular drones, ornithopters, and genuine birds. The CNN (Convolutional Neural Network) method, created using the Google Cloud Platform and AutoML widget, is used in our model for object identification and categorization. DroneSilient has an RF sensor that can identify and imitate the threat presented by recognized drones. Convolutional Network, Modified Blooms Algorithm, RFID, and RF Sensor systems are all integrated into the DroneSilient system as part of this methodology combination, which provides a thorough method for identifying and eliminating drone threats. The bloom filter proposed takes 27.6 to 12.4 microseconds. Overall time for handling the unauthorized drone will be less than 180 s. By tackling every facet of the problem, our strategy outperforms many current anti-drone solutions in terms of functionality.https://doi.org/10.1186/s40537-024-01004-6Anti-drone systemsDrone detectionConvolutional neural networkJammingRFID |
| spellingShingle | Meghna Manoj Nair Harini Sriraman Gadiparthy Harika Sai V. Pattabiraman DroneSilient (drone + resilient): an anti-drone system Journal of Big Data Anti-drone systems Drone detection Convolutional neural network Jamming RFID |
| title | DroneSilient (drone + resilient): an anti-drone system |
| title_full | DroneSilient (drone + resilient): an anti-drone system |
| title_fullStr | DroneSilient (drone + resilient): an anti-drone system |
| title_full_unstemmed | DroneSilient (drone + resilient): an anti-drone system |
| title_short | DroneSilient (drone + resilient): an anti-drone system |
| title_sort | dronesilient drone resilient an anti drone system |
| topic | Anti-drone systems Drone detection Convolutional neural network Jamming RFID |
| url | https://doi.org/10.1186/s40537-024-01004-6 |
| work_keys_str_mv | AT meghnamanojnair dronesilientdroneresilientanantidronesystem AT harinisriraman dronesilientdroneresilientanantidronesystem AT gadiparthyharikasai dronesilientdroneresilientanantidronesystem AT vpattabiraman dronesilientdroneresilientanantidronesystem |